# Publications Freek Stulp

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 • Sorted by Date • Classified by Publication Type • Classified by Research Category • Emergent Proximo-Distal Maturation through Adaptive Exploration Freek Stulp and Pierre-Yves Oudeyer. Emergent Proximo-Distal Maturation through Adaptive Exploration. In International Conference on Development and Learning (ICDL), 2012. Paper of Excellence Award Download [PDF]912.1kB Abstract Life-long robot learning in the high-dimensional real world requires guided and structured exploration mechanisms. In this developmental context, we investigate here the use of the recently proposed PI2-CMAES episodic reinforcement learning algorithm, which is able to learn high-dimensional motor tasks through adaptive control of exploration. By studying PI2-CMAES in a reaching task on a simulated arm, we observe two developmental properties. First, we show how PI2-CMAES autonomously and continuously tunes the global exploration/exploitation trade-off, allowing it to re-adapt to changing tasks. Second, we show how PI2-CMAES spontaneously self-organizes a maturational structure whilst exploring the degrees-of-freedom (DOFs) of the motor space. In particular, it automatically demonstrates the so-called proximo-distal maturation observed in humans: after first freezing distal DOFs while exploring predominantly the most proximal DOF, it progressively frees exploration in DOFs along the proximo-distal body axis. These emergent properties suggest the use of PI2-CMAES as a general tool for studying reinforcement learning of skills in life-long developmental learning contexts. BibTeX
 @InProceedings{stulp12emergent, title = {Emergent Proximo-Distal Maturation through Adaptive Exploration}, author = {Freek Stulp and Pierre-Yves Oudeyer}, booktitle = {International Conference on Development and Learning (ICDL)}, year = {2012}, note = {{\bf Paper of Excellence Award}}, abstract = {Life-long robot learning in the high-dimensional real world requires guided and structured exploration mechanisms. In this developmental context, we investigate here the use of the recently proposed PI2-CMAES episodic reinforcement learning algorithm, which is able to learn high-dimensional motor tasks through adaptive control of exploration. By studying PI2-CMAES in a reaching task on a simulated arm, we observe two developmental properties. First, we show how PI2-CMAES autonomously and continuously tunes the global exploration/exploitation trade-off, allowing it to re-adapt to changing tasks. Second, we show how PI2-CMAES spontaneously self-organizes a maturational structure whilst exploring the degrees-of-freedom (DOFs) of the motor space. In particular, it automatically demonstrates the so-called \emph{proximo-distal maturation} observed in humans: after first freezing distal DOFs while exploring predominantly the most proximal DOF, it progressively frees exploration in DOFs along the proximo-distal body axis. These emergent properties suggest the use of PI2-CMAES as a general tool for studying reinforcement learning of skills in life-long developmental learning contexts.}, bib2html_pubtype = {Refereed Conference Paper,Awards}, bib2html_rescat = {Reinforcement Learning of Robot Skills} } 

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